code: def __init__(
      self,
      dataset,
      train_info,
      train,
      batch_size,
      sort_in_buckets=None,
      device=None,
      **kwargs,
  ):
      if sort_in_buckets is None:
          sort_in_buckets = train
      self.sort_in_buckets = sort_in_buckets
      self.train_info = train_info
      super(MatchingIterator, self).__init__(
          dataset,
          batch_size,
          train=train,
          repeat=False,
          sort=False,
          device=device,
          **kwargs,
      )
  
file_path: deepmatcher/deepmatcher/data/iterator.py
imports: from __future__ import division
  import logging
  from torchtext.legacy import data
  from ..batch import MatchingBatch
label_desc: Use image node_id 1 to create a new MatchingIterator object from inherited base classes: data with example: obj = MatchingIterator(dataset, train_info, train, batch_size, sort_in_buckets, device)